US Gas and Oil Fields¶

Data Import¶

In [1]:
import pandas as pd
import matplotlib.pyplot as plt
import geopandas as gpd
import folium
import contextily as cx
import rtree
from zlib import crc32
import hashlib
from shapely.geometry import Point, LineString, Polygon
/Users/jnapolitano/venvs/finance/lib/python3.9/site-packages/geopandas/_compat.py:111: UserWarning: The Shapely GEOS version (3.10.2-CAPI-1.16.0) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.
  warnings.warn(

Oil and Natural Gas Field Data¶

In [2]:
## Importing our DataFrames

gisfilepath = "/Users/jnapolitano/Projects/data/energy/Oil_and_Natural_Gas_Fields.geojson"

fields_df = gpd.read_file(gisfilepath)
na = fields_df.PR_OIL.min()
fields_df.replace(na, 0 , inplace=True)


fields_df = fields_df.to_crs(epsg=3857)

fields_df.describe()
Out[2]:
OBJECTID PR_OIL PR_GAS SHAPE_Length SHAPE_Area
count 224.000000 224.000000 224.000000 224.000000 224.000000
mean 112.500000 1530.496585 87.685987 16.473665 10.386605
std 64.807407 17219.764834 644.145040 43.284473 45.170003
min 1.000000 0.000000 0.000000 0.100238 0.000594
25% 56.750000 0.000000 0.000000 2.511389 0.221885
50% 112.500000 0.000000 0.000000 6.563044 1.225873
75% 168.250000 0.000000 0.000000 14.317886 5.378536
max 224.000000 238050.000000 8446.000000 485.692251 448.052251
{eval-rst}

.. index::
   single: Oil/Gas Fields Map by Commodity

Oil Gas Field Map by Commodity¶

In [3]:
fields_map =fields_df.explore(
    column="COMMODITY", # make choropleth based on "PORT_NAME" column
     popup=False, # show all values in popup (on click)
     tiles="Stamen Terrain", # use "CartoDB positron" tiles
     cmap='Reds', # use "Set1" matplotlib colormap
     #style_kwds=dict(color="black"),
     marker_kwds= dict(radius=6),
     tooltip=['NAICS_DESC','REGION', 'COMMODITY' ],
     legend =True, # use black outline)
     categorical=True,
    )


fields_map
Out[3]:
Make this Notebook Trusted to load map: File -> Trust Notebook